Integrated analysis of co‐expression and ceRNA network identifies five lncRNAs as prognostic markers for breast cancer

Long non‐coding RNAs (lncRNAs), which competitively bind miRNAs to regulate target mRNA expression in the competing endogenous RNAs (ceRNAs) network, have attracted increasing attention in breast cancer research. We aim to find more effective therapeutic targets and prognostic markers for breast cancer. LncRNA, mRNA and miRNA expression profiles of breast cancer were downloaded from TCGA database. We screened the top 5000 lncRNAs, top 5000 mRNAs and all miRNAs to perform weighted gene co‐expression network analysis. The correlation between modules and clinical information of breast cancer was identified by Pearson's correlation coefficient. Based on the most relevant modules, we constructed a ceRNA network of breast cancer. Additionally, the standard Kaplan‐Meier univariate curve analysis was adopted to identify the prognosis of lncRNAs. Ultimately, a total of 23 and 5 modules were generated in the lncRNAs/mRNAs and miRNAs co‐expression network, respectively. According to the Green module of lncRNAs/mRNAs and Blue module of miRNAs, our constructed ceRNA network consisted of 52 lncRNAs, 17miRNAs and 79 mRNAs. Through survival analysis, 5 lncRNAs (AL117190.1, COL4A2‐AS1, LINC00184, MEG3 and MIR22HG) were identified as crucial prognostic factors for patients with breast cancer. Taken together, we have identified five novel lncRNAs related to prognosis of breast cancer. Our study has contributed to the deeper understanding of the molecular mechanism of breast cancer and provided novel insights into the use of breast cancer drugs and prognosis.     

Circular RNA-associated ceRNA network involved in HIF-1 signalling in triple-negative breast cancer: circ_0047303 as a potential key regulator

The aggressive and highly metastatic nature of triple-negative breast cancer (TNBC) causes patients to suffer from the poor outcome. HIF-1 signalling pathway is a prominent pathway that contributes to angiogenesis and metastasis progression in tumours. On the contrary, the undeniable importance of circular RNAs (circRNAs) as multifunctional non-coding RNAs (ncRNAs) has been identified in breast cancer. These ncRNAs owing to their high stability and specificity have been becoming a hotspot in cancer researches. circRNAs act as competing endogenous RNAs (ceRNAs) and compete with mRNAs for shared miRNAs, thus modulate gene expression. Since the most dysregulated biological functions in TNBC are associated with cellular invasion, understanding the molecular pathogenesis of these processes is a crucial step towards the development of new treatment approaches. The purpose of this study is to undermine the circRNA-associated ceRNA network involved in HIF-1 signalling in TNBC using an integrative bioinformatics approach. In the next step, the novel circ_0047303-mediated ceRNA regulatory axes have been extracted and validated across TNBC samples. We show that circ_0047303 has the highest degree in the circRNA-associated ceRNA network and shows a significant up-expression in TNBC. Moreover, our results suggest that circ_0047303 could mediate the upregulation of key angiogenesis-related genes, including HIF-1, EIF4E2 and VEGFA in TNBC through sponging the tumour-suppressive miRNAs. The circ_0047303 could be a promising molecular biomarker and/or therapeutic target for TNBC.     

Predicting functional circular RNA-based competitive endogenous RNA network in gastric carcinoma using novel bioinformatics analysis

Gastric cancer (GC) remains one of the most prevalent types of malignancies worldwide, and also one of the most reported lethal tumor-related diseases. Circular RNAs (circRNAs) have been certified to be trapped in multiple aspects of GC pathogenesis. Yet, the mechanism of this regulation is mostly undefined. This research is designed to discover the vital circRNA-microRNA (miRNA)-messenger RNA (mRNA) regulatory network in GC. Expression profiles with diverse levels including circRNAs, miRNAs, and mRNAs were all determined using microarray public datasets from Gene Expression Ominous (GEO). The differential circRNAs expressions were recognized against the published robust rank aggregation algorithm. Besides, a circRNA-based competitive endogenous RNA (ceRNA) interaction network was visualized via Cytoscape software (version 3.8.0). Functional and pathway enrichment analysis associated with differentially expressed targeted mRNAs were conducted using Cytoscape and an online bioinformatics database. Furthermore, an interconnected protein–protein interaction association network which consisted of 51 mRNAs was predicted, and hub genes were screened using STRING and CytoHubba. Then, several hub genes were chosen to explore their expression associated with survival rate and clinical stage in GEPIA and Kaplan-Meier Plotter databases. Finally, a carefully designed circRNA-related ceRNA regulatory subnetwork including four circRNAs, six miRNAs, and eight key hub genes was structured using the online bioinformatics tool.     

Identification of downregulated circRNAs from tissue and plasma of patients with gastric cancer and construction of a circRNA-miRNA-mRNA network

The connection between circular RNAs (circRNAs) and gastric cancer has been reported widely in recent years. However, previous studies have focused mainly on circRNAs from gastric cancer tissue. The objectives of the present study were to detect dysregulated circRNAs from both tissue and plasma of patients with gastric cancer and to explore their potential roles in the pathogenesis of gastric cancer. Expression profiles of circRNAs were obtained from the Gene Expression Omnibus (GEO) and analyzed using the GEO2R tool to identify differential expressed circRNAs. The significance threshold was set as |log2 (fold change)| > 2 and adjusted P < .05. The microRNA (miRNA) binding sites of the differentially expressed circRNAs were predicted using the Circular RNA Interactome web tool. TargetScan and the miRNet database were used to predict the miRNA target genes. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses were performed using Database for Annotation Visualization and Integrated Discovery. Hub genes were identified and a network was constructed with Cytoscape. The overall survival rates for the selected miRNAs and messenger RNAs were evaluated by Kaplan-Meier Plotter. A total of three downregulated circRNAs (hsa_circ_0001190, hsa_circ_0036287, and hsa_circ_0048607) were identified in this study. Six miRNAs and eight hub genes met the significance criteria and were selected for further analysis. A circRNA-miRNA-hub gene network was constructed based on three circRNAs, six miRNAs, and eight hub genes. Evaluation of overall survival rates for the hub genes showed that low expression levels of GADD45A, PPP1CB, PJA2, and KLF2 were associated with poor overall survival. This study identified potential novel plasma circRNA biomarkers and provides insights into the underlying mechanisms of gastric cancer pathogenesis.     

转录因子及miRNA调控食管癌耐药机制研究

由于耐药性的存在,不同患者在使用相同药物时会导致治疗效果的差异.因此识别耐药性相关的关键生物学标记,有助于临床医生快速选择出适合的药物,延长患者的生存时间,对药物研发以及药物的作用机制的详细研究具有重要意义.首先在食管癌细胞系中筛选不同药物的耐药及敏感细胞系,从中找到不同药物耐药相关的基因,将这些计算得到的耐药相关基因...     

An evaluation of gastric adenocarcinoma-associated CircRNAs based on microarray meta-analysis and ceRNA networks

Gastric cancer is the fourth leading cause of cancer-related mortality and one of the most commonly diagnosed malignancies worldwide. Gastric adenocarcinoma (GAC) accounts for the majority of gastric cancer cases. Circular RNAs (circRNAs) have been shown to be associated with carcinogenesis and cancer progression. This research aims to investigate GAC-associated circRNAs and the underlying mechanisms of circRNA-miRNA-mRNA networks in the development and progression of GAC. Differentially expressed miRNAs and mRNAs (DEMs and DEGs) were identified in Gene Expression Omnibus (GEO) microarray datasets using the R package Limma. A microarray meta-analysis was performed to identify potential GAC-associated circRNAs with high statistical power, resulting in 13 up-regulated and 19 down-regulated circRNAs. CircRNA-miRNA-mRNA networks were constructed by combining predicted and experimentally validated databases and considering differentially expressed miRNAs and mRNAs. The constructed ceRNA networks revealed the potential regulatory effect of hsa_circ_0002019 and hsa_circ_0074736 on key survival-related genes. The expression levels of these two circRNAs were measured in plasma samples from GAC patients and healthy controls using SYBR Green-based real-time PCR. Axon guidance, cellular senescence, AGE-RAGE signaling pathway in diabetic complications, and AMPK signaling pathway were among the major significant (P-value <0.05) enriched pathways of "main mRNAs" in the constructed ceRNA networks. In conclusion, we identified strongly correlated circRNAs and their likely mechanisms of action in GAC, which may improve the knowledge of regulatory networks underlying GAC formation and contribute to developing better strategies for early diagnosis, prognosis, and treatment.     

Long noncoding RNA HOXA-AS2 regulates the expression of SCN3A by sponging miR-106a in breast cancer

Breast cancer is the most commonly diagnosed cancer that affects women worldwide. This study aimed to investigate the competing endogenous RNAs (ceRNAs) mechanism in breast cancer. Microarray data were downloaded from the University of California Santa Cruz (UCSC) Xena database. The limma package was used to screen the differentially expressed messenger RNAs (DEMs) and differentially expressed long noncoding RNAs (DELs). Subsequently, functional analysis was performed using DAVID tool. After constructing the protein-protein interaction (PPI) network, we identified the major gene modules using the Cytoscape software. Univariate survival analysis in the survival package was performed. Finally, the ceRNA regulatory network was constructed to identify the critical genes. A total of 1380 DEMs and 345 DELs were identified in breast cancer samples compared with normal samples. Functional enrichment analysis showed that DEMs were mainly involved in cell division, and cell cycle. We screened four major gene modules and identified the hub nodes in these functional modules. Several DEMs (including FABP7, C4BPA, and LAMB3) and three long noncoding RNAs (lncRNAs) (LINC00092, SLC26A4.AS1, and COLCA1) exhibited significant correlation with patients' survival outcomes. In the ceRNA network, the lncRNA HOXA-AS2 regulated the expression level of SCN3A by interacting with hsa-miR-106a-5p. Thus, our study investigated the ceRNA mechanism in breast cancer. The results showed that lncRNA HOXA-AS2 might modulate the expression of SCN3A by sponging miR-106a in breast cancer.     

急性髓系白血病mRNA与非编码RNA差异表达谱及CeRNA调控网络分析

利用GEO数据库(gene expression omnibus database)通过生物信息学分析方法探讨急性髓系白血病(acute myelogenous leukemia,AML)的发病机制。检索GEO数据库中AML相关芯片数据集GSE142698、GSE142699和GSE96535。利用GEO2R分析得到差异mRNAs、miRNAs以及差异lncRNAs。利用在线生物信息学分析工具DAVID对差异mRNAs进行GO富集分析和KEGG通路分析。利用miRWalk数据库预测AML相关miRNAs的靶向mRNAs,利用Spongescan数据库预测AML相关miRNAs的靶向lncRNAs,构建lncRNA-miRNA-mRNA竞争性内源RNA （competing endogenous RNA,ceRNA）调控网络。共筛选出29个显著差异mRNAs、70个显著差异miRNAs和20 005个显著差异lncRNAs。GO富集分析和KEGG通路分析显示,差异表达基因主要涉及蛋白磷酸化、细胞分裂、细胞增殖的负调控、基因表达的正向调节、周期蛋白依赖的丝氨酸/苏氨酸激酶活性的调节等生物过程以及细胞周期、细胞衰老、癌症通路、PI3K-Akt通路等信号通路。将miRWalk数据库预测的靶向mRNAs与差异mRNAs取交集,Spongescan数据库预测的靶向lncRNAs与差异lncRNAs取交集,分别确定了25个mRNAs、6个lncRNAs参与AML相关ceRNA调控网络的构建。结果表明,lncRNAs可能作为关键的ceRNA,通过调控miRNA和相关靶基因参与AML的发生与发展,研究结果为AML诊断和治疗的分子生物学研究提供了新的依据。     

Multiple types of noncoding RNA are involved in potential modulation of PTTG1's expression and function in breast cancer

Breast cancer is a malignant type with morbidity ranking the first of women globally. As widely acknowledged, there exist close links between ncRNA-mRNA axis and breast cancer. In this study, we first overviewed expression and prognostic values of pituitary tumor transforming gene (PTTGs) in breast cancer. Next, two binding miRNAs (miR-186-5p and miR-655-3p) of PTTG1 in breast cancer were identified. Subsequently, several potential upstream ncRNAs of PTTG1-miR-186-5p/miR-655-3p axis in breast cancer were successively screened out, consisting of 11 lncRNAs, 17 circRNAs and 12 pseudogene-derived RNAs. Enrichment analysis for downstream target genes of PTTG1-miR-186-5p/miR-655-3p axis revealed that this axis is associated with TGF-beta signaling and MAPK signaling pathways. Further investigation demonstrated AURKA was one of the most key hub genes. Collectively, we established a potential PTTG1-related ncRNA-mRNA regulatory network in breast cancer.     

Biomarker potentials of miRNA-associated circRNAs in breast cancer (MCF-7) cells: an in vitro and in silico study

Breast cancer is a heterogeneous disease, which is the most common malignancy in women. The incidence and mortality rates of breast cancer indicate that it is the leading cause of cancer-related with deaths. circRNAs operate as part of competing endogenous RNAs (ceRNAs) mechanisms, which play critical roles in the different biological processes of breast cancer such as proliferation, migration, and apoptosis. The goal of the present study is to identify the potential predictive biomarker for breast cancer diagnosis in the circRNA network by in vitro and in silico analyzes. 40 miRNAs were obtained from the miRWalk database and their combinatorial target genes (potential ceRNAs) were identified with ComiR. We stated that the cancer-specific circRNA genes in MCF-7 cells using the cancer-specific circRNA (CSDC) database, and obtained the ones showing potential ceRNA activity in our previous analysis among them. Identified genes with remarkable expression differences between BCa and normal breast tissue were determined by the GEPIA database. Moreover, the Spearman correlation test in the GEPIA database was used for the statistical analysis of the relationship between DCAF7 and SOGA1, SOGA1 and AVL 9, DCAF7 and AVL 9 gene pairs. And also, DCAF7, SOGA1, and AVL9 gene expression levels were detected in MCF-7 and MCF-10A cells by RT-qPCR method. DCAF7, SOGA1, and AVL9 gene were significantly more expressed to BCa tissue and MCF-7 cells than normal breast tissue and MCF-10 A cells. And also, DCAF7 and SOGA1, SOGA1 and AVL9, DCAF7 and AVL9 genes pairs were found to be significantly correlated with BCa. These genes may be considered as potential predictive biomarkers to discriminate BCa patients from healthy persons. Our preliminary results can supply a new perspective for in vitro and vivo studies in the future.

     

To construct a ceRNA regulatory network as prognostic biomarkers for bladder cancer

Emerging evidence demonstrates that competing endogenous RNA (ceRNA) hypothesis has played a role in molecular biological mechanisms of cancer occurrence and development. But the effect of ceRNA network in bladder cancer (BC), especially lncRNA‐miRNA‐mRNA regulatory network of BC, was not completely expounded. By means of The Cancer Genome Atlas (TCGA) database, we compared the expression of RNA sequencing (RNA‐Seq) data between 19 normal bladder tissue and 414 primary bladder tumours. Then, weighted gene co‐expression network analysis (WGCNA) was conducted to analyse the correlation between two sets of genes with traits. Interactions between miRNAs, lncRNAs and target mRNAs were predicted by MiRcode, miRDB, starBase, miRTarBase and TargetScan. Next, by univariate Cox regression and LASSO regression analysis, the 86 mRNAs obtained by prediction were used to construct a prognostic model which contained 4 mRNAs (ACTC1 + FAM129A + OSBPL10 + EPHA2). Then, by the 4 mRNAs in the prognostic model, a ceRNA regulatory network with 48 lncRNAs, 14 miRNAs and 4 mRNAs was constructed. To sum up, the ceRNA network can further explore gene regulation and predict the prognosis of BC patients.     

意大利蜜蜂工蜂中肠的环状RNA及其调控网络分析

【目的】环状RNA(circRNA)在可变剪接、转录调控和来源基因的表达调控等方面具有重要功能。本研究旨在分析意大利蜜蜂Apis mellifera ligustica工蜂中肠circRNA的数量、种类、结构特征和作用,并通过构建和分析circRNA的调控网络探索circRNA的调控功能。【方法】在实验室条件下人工饲养意大利蜜蜂工蜂,利用circRNA-seq技术对意大利蜜蜂7和10日龄成年工蜂中肠样品进行深度测序。利用find_circ软件从质控后的数据中预测circRNA。通过BLAST比对GO和KEGG数据库,对circRNA的来源基因进行功能和代谢通路注释。利用TargetFinder软件预测circRNA靶向结合的miRNA及miRNA靶向结合的mRNA,通过Cytoscape v.3.2.1软件对circRNA-miRNA和circRNA-miRNA-mRNA调控网络进行构建及可视化。通过设计背靠背引物和线性扩增引物RT-PCR对预测出的circRNA进行验证。【结果】意大利蜜蜂工蜂中肠样品的测序平均得到136 463 071条clean reads,去除rRNA后各样品的anchor reads均在136 779 122条及以上。共预测出10 833个circRNA,长度主要介于15~1 000 nt;上述circRNA的类型丰富,其中已注释的外显子circRNA数量最多,分布在西方蜜蜂1号染色体的circRNA数量最多,其次为8号染色体。CircRNA的来源基因可注释到包括结合、细胞进程和细胞在内的45个GO条目,以及包括内吞作用、内质网蛋白加工及核糖体在内的121条KEGG代谢通路,表明circRNA在意大利蜜蜂工蜂中肠的生长、发育、新陈代谢和细胞生命活动等生物学过程中发挥重要作用。进一步构建circRNA-miRNA和circRNA-miRNA-mRNA调控网络,分析结果显示部分circRNA可能作为竞争性内源RNA吸附结合microRNA,从而调控基因的表达水平。最后,对随机选择的3个circRNA的RT-PCR结果验证了其真实存在。【结论】本研究对意大利蜜蜂工蜂中肠中的circRNA进行预测、分析及鉴定。研究结果提供了中肠circRNA的数量、种类、结构特征、作用和调控网络的信息,揭示了circRNA可能通过作用于来源基因和作为竞争性内源RNA在意大利蜜蜂工蜂中肠的生长发育和免疫防御中发挥作用,为深入研究circRNA在意大利蜜蜂中肠发育及胁迫响应过程中的功能奠定了基础。     

Integrated analysis of ceRNA network and tumor-infiltrating immune cells in esophageal cancer

Background: Esophageal cancer (ESCA) is one of the most commonly diagnosed cancers in the world. Tumor immune microenvironment is closely related to tumor prognosis. The present study aimed at analyzing the competing endogenous RNA (ceRNA) network and tumor-infiltrating immune cells in ESCA.Methods: The expression profiles of mRNAs, lncRNAs, and miRNAs were downloaded from the Cancer Genome Atlas database. A ceRNA network was established based on the differentially expressed RNAs by Cytoscape. CIBERSORT was applied to estimate the proportion of immune cells in ESCA. Prognosis-associated genes and immune cells were applied to establish prognostic models basing on Lasso and multivariate Cox analyses. The survival curves were constructed with Kaplan–Meier method. The predictive efficacy of the prognostic models was evaluated by the receiver operating characteristic (ROC) curves.Results: The differentially expressed mRNAs, lncRNAs, and miRNAs were identified. We constructed the ceRNA network including 23 lncRNAs, 19 miRNAs, and 147 mRNAs. Five key molecules (HMGB3, HOXC8, HSPA1B, KLHL15, and RUNX3) were identified from the ceRNA network and five significant immune cells (plasma cells, T cells follicular helper, monocytes, dendritic cells activated, and neutrophils) were selected via CIBERSORT. The ROC curves based on key genes and significant immune cells all showed good sensitivity (AUC of 3-year survival: 0.739, AUC of 5-year survival: 0.899, AUC of 3-year survival: 0.824, AUC of 5-year survival: 0.876). There was certain correlation between five immune cells and five key molecules.Conclusion: The present study provides an effective bioinformatics basis for exploring the potential biomarkers of ESCA and predicting its prognosis.     

Discovery of microRNA-mRNA modules via population-based probabilistic learning

MOTIVATION: MicroRNAs (miRNAs) and mRNAs constitute an important part of gene regulatory networks, influencing diverse biological phenomena. Elucidating closely related miRNAs and mRNAs can be an essential first step towards the discovery of their combinatorial effects on different cellular states. Here, we propose a probabilistic learning method to identify synergistic miRNAs involving regulation of their condition-specific target genes (mRNAs) from multiple information sources, i.e. computationally predicted target genes of miRNAs and their respective expression profiles. RESULTS: We used data sets consisting of miRNA-target gene binding information and expression profiles of miRNAs and mRNAs on human cancer samples. Our method allowed us to detect functionally correlated miRNA-mRNA modules involved in specific biological processes from multiple data sources by using a balanced fitness function and efficient searching over multiple populations. The proposed algorithm found two miRNA-mRNA modules, highly correlated with respect to their expression and biological function. Moreover, the mRNAs included in the same module showed much higher correlations when the related miRNAs were highly expressed, demonstrating our method's ability for finding coherent miRNA-mRNA modules. Most members of these modules have been reported to be closely related with cancer. Consequently, our method can provide a primary source of miRNA and target sets presumed to constitute closely related parts of gene regulatory pathways.     

Identification and characterization of tumorigenic circular RNAs in cervical cancer

Circular RNAs (circRNAs) are a distinctive family of ncRNAs, and they function as key regulators in the initiation, development and progression of various diseases. However, the regulatory roles of circRNAs in the tumorigenesis of cervical cancer (CC) have not been fully understood. In this study, we identified a set of circRNAs in CC and paired normal tissues, using RNA sequencing data, and found that the cancer and normal tissues could be told apart by those differentially expressed (DE) circRNAs, indicating that circRNA expression profiles in CC were significantly different from those in the normal tissues. Meanwhile, the upregulated genes in CC were enriched in inflammation-related pathways, and the correlation analysis between the DE circRNAs and genes revealed that the abundance of DE circRNAs was positively related to the expression of their host genes. However, the expression of TGFBR2 and KDM4C were found to exhibit a negative correlation with their corresponding circRNAs. Furthermore, we also predicted the interactions between circRNAs and proteins, and constructed a competing endogenous RNA (ceRNA) network. Specifically, hsa_circ_0001495 was predicted to interact with ESRP2, and acted as a sponge by competing for miRNAs with TBL1XR1. Functionally, hsa_circ_0001495 was predicted to regulate epithelial cell proliferation and NOTCH signaling via ESRP2 and TBL1XR1, respectively. We also evaluated the prognostic values of downstream target genes of selected circRNAs, using clinical records of CC patients. In summary, the present study provided some regulatory circRNAs involved in CC tumorigenesis based on bioinformatics approaches, which brought strong evidences for researchers to further explore their biological and clinical values.     

Human microRNA similarity in breast cancer

MicroRNAs (miRNAs) play important roles in a variety of human diseases, including breast cancer. A number of miRNAs are up- and down-regulated in breast cancer. However, little is known about miRNA similarity and similarity network in breast cancer. Here, a collection of 272 breast cancer-associated miRNA precursors (pre-miRNAs) were utilized to calculate similarities of sequences, target genes, pathways and functions and construct a combined similarity network. Well-characterized miRNAs and their similarity network were highlighted. Interestingly, miRNA sequence-dependent similarity networks were not identified in spite of sequence–target gene association. Similarity networks with minimum and maximum number of miRNAs originate from pathway and mature sequence, respectively. The breast cancer-associated miRNAs were divided into seven functional classes (classes I–VII) followed by disease enrichment analysis and novel miRNA-based disease similarities were found. The finding would provide insight into miRNA similarity, similarity network and disease heterogeneity in breast cancer.